Multivariate Statistical Process Control (MSPC) seeks to monitor several quality characteristics simultaneously. However, it has limitations derived from its inability to identify the source of special variation in the process. In this research, a proposed model that does not have this limitation is presented. In this paper, data from two scenarios were used: (A) data created by simulation and (B) random variable data obtained from the analysed product, which in this case corresponds to cheese production slicing process in the dairy industry. The model includes a dimensional reduction procedure based on the centrality and data dispersion. The goal is to recognise a multivariate pattern from the conjunction of univariate variables with variation patterns so that the model indicates the univariate patterns from the multivariate pattern. The model consists of two stages. The first stage is concerned with the identification process and uses Moving Windows (MWs) for data segmentation and pattern analysis. The second stage uses Bayesian Inference techniques such as conditional probabilities and Bayesian Networks. By using these techniques, the univariate variable that contributed to the pattern found in the multivariate variable is obtained. Furthermore, the model evaluates the probability of the patterns of the individual variables generating a specific pattern in the multivariate variable. This probability is interpreted as a signal of the performance of the process that allows to identify in the process a multivariate out-of-control state and the univariate variable that causes the failure. The efficiency results of the proposed model compared favourably with respect to the results obtained using the Hotelling’s $T^2$ chart, which validates our model.

Industrial processes seek to improve their quality control, including new technologies and satisfying requirements for globalised markets. In this paper, we present an innovative method based on Multivariate Pattern Recognition (MVPR) and process monitoring in a real-world study case. By identifying a distinctive out-of-control multivariate pattern using the Support Vector Machines (SVM) and the Mahalanobis Distance $D^2$ it is possible to infer the variables that disturbed the process; hence, possible faults can be predicted knowing the state of the process. The method is based on our previous work, and in this paper we present the method application for an automated process, namely, the robotic Gas Metal Arc Welding (GMAW). Results from the application indicate an overall accuracy up to $88.8\%$, which demonstrates the effectiveness of the method, which can also be used in other MVPR tasks.

Multivariate control graphics detect signals out of control of the process. These signals are special patterns of joint variation, but they do not allow to determine what types of variation patterns take place in the individual variables. The referred problem has been treated through models of pattern recognition (PR) by Artificial Neural Networks (ANN). There are important advances in solving the problem to univariate cases, but not so in multivariate cases. There is no research which affirms that a single ANN can identify a multivariate out-of-control signal and recognize the special variation types of the variables individually. This research presents a model of PR of special variation in bivariate processes, and is based on an organized assembly of different types of ANN which are activated sequentially. With this work, it is possible to obtain a diagnosis of the bivariate process control that simultaneously recognizes the type of variation of the variables involved. This novel model provides the basis of new knowledge about statistical control of bivariate processes by PR through ANN. The model had two stages of training: experimental and industrial. The first one worked with data generated by Montecarlo simulation and the second one with data from a process that performs manufactured operations on metal bars used in the speed system in automobiles.

Preliminary activities of searching and selecting relevant articles are crucial in scientific research to determine the state of the art (SOTA) and enhance overall outcomes. While there are automatic tools for keyword extraction, these algorithms are often computationally expensive, storage-intensive, and reliant on institutional subscriptions for metadata retrieval. Most importantly, they still require manual selection of literature. This paper introduces a framework that automates keyword searching in article abstracts to help select relevant literature for the SOTA by identifying key terms matching that we, hereafter, call source words. A case study in the food and beverage industry is provided to demonstrate the algorithm’s application. In the study, five relevant knowledge areas were defined to guide literature selection. The database from scientific repositories was categorized using six classification rules based on impact factor (IF), Open Access (OA) status, and JCR journal ranking. This classification revealed the knowledge area with the highest presence and highlighted the effectiveness of the selection rules in identifying articles for the SOTA. The approach included a panel of experts who confirmed the algorithm’s effectiveness in identifying source words in high-quality articles. The algorithm’s performance was evaluated using the $F_1$ Score, which reached 0.83 after filtering out non-relevant articles. This result validates the algorithm’s ability to extract significant source words and demonstrates its usefulness in building the SOTA by focusing on the most scientifically impactful articles.

In this paper is proposed a method of multivariate pattern recognition applied in a industrial case of statistical process control with two quality characteristics. The essence of functioning of the method is the suitable determination of the values of the weights of the activation function (FA) of the artificial neural network (RNA) perceptron, so that without using the algorithms of this training set is used dimensional transformer as an asset. The application of the FA of the perceptron with the approach used in this research, is novel and an alternative to the common use of the same. The industrially proven method proved to be of relevance in the multivariate statistical process control. The bivariate case was analyzed by combinations of patterns that compose the variables X1 and X2 that integrate the binary variable X = [X1, X2]. The experimental and industrially analyzed patterns for these variables were found in the real case study and are: natural, increasing trend, decreasing trend, upward shift and downward shift. The results show a high rate of recognition of patterns in the random variables that comprise each bivariate combination.